Jumping over the network threshold of information diffusion
Testing the threshold hypothesis of social influence
Bibliographic Data
| ID | 12420678 |
|---|---|
| Authors | Chengjun Wang (0000-0001-8433-3512, corresponding author), Cheng-Jun Wang (0000-0002-9507-2888, Nanjing University, corresponding author), Jonathan J H Zhu (0000-0001-6173-6941, City University of Hong Kong) |
| Year | 2021 |
| Volume | 31 |
| Issue | 5 |
| Pages | 1677-1694 |
| Publication date | 2021-02-18 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Internet Research (JOURNAL) |
| Journal identifiers | ISSN: 1066-2243 • E-ISSN: 2054-5657 |
| Publisher | Emerald Publishing Limited (PUBLISHER • GB) |
| DOI | 10.1108/intr-08-2019-0313 |
| OpenAlex | W3130786692 |
| Language | EN |
| Citations received | 4 |
| References cited | 30 |
Purpose Social influence plays a crucial role in determining the size of information diffusion. Drawing on threshold models, we reformulate the nonlinear threshold hypothesis of social influence. Design/methodology/approach We test the threshold hypothesis of social influence with a large dataset of information diffusion on social media. Findings There exists a bell-shaped relationship between social influence and diffusion size. However, the large network threshold, limited diffusion depth and intense bursts become the bottlenecks that constrain the diffusion size. Practical implications The practice of viral marketing needs innovative strategies to increase information novelty and reduce the excessive network threshold. Originality/value In all, this research extends threshold models of social influence and underlines the nonlinear nature of social influence in information diffusion
Diffusion · Econometrics · Economics · Novelty · Originality · Physics · Social media · Social network (sociolinguistics · Statistical physics · Threshold model · Complex Network Analysis Techniques · Computer Science · Innovation Diffusion and Forecasting · Opinion Dynamics and Social Influence · Psychology · Social Psychology
A simple model of global cascades on random networks
Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks
The Spread of Behavior in an Online Social Network Experiment
The Structural Virality of Online Diffusion
Opinion Leadership and Social Contagion in New Product Diffusion
Sampling Methods for Random Digit Dialing
Broadcast Versus Viral Spreading
The tweet smell of celebrity success
A Random Digit Search (RDS) Method for Sampling of Blogs and Other User-Generated Content
Threshold models of diffusion and collective behavior
The politics of 'platforms
Threshold Models of Collective Behavior
The Strength of Weak Ties
| Citations per year | 1 |
|---|---|
| Citation span | 2022 - 2024 (3) |
| Highly cited | No |